What is he best known for?
The fields of study he is best known for:
- Internal medicine
- Artificial intelligence
- Radiology
His primary areas of investigation include Artificial intelligence, Pattern recognition, Ultrasound, Segmentation and Computer vision.
His Pattern recognition research incorporates elements of Speech recognition and Disease, Fatty liver.
His Ultrasound research is classified as research in Radiology.
His work carried out in the field of Radiology brings together such families of science as Internal medicine and CAD.
His Segmentation study combines topics in areas such as 2 d ultrasound, Carotid arteries, Robustness and Region of interest.
When carried out as part of a general Support vector machine research project, his work on Polynomial kernel is frequently linked to work in Probabilistic neural network, therefore connecting diverse disciplines of study.
His most cited work include:
- Automated diagnosis of epileptic EEG using entropies (385 citations)
- Review: A state of the art review on intima-media thickness (IMT) measurement and wall segmentation techniques for carotid ultrasound (172 citations)
- US-guided percutaneous radiofrequency thermal ablation for the treatment of solid benign hyperfunctioning or compressive thyroid nodules (134 citations)
What are the main themes of his work throughout his whole career to date?
Artificial intelligence, Ultrasound, Segmentation, Computer vision and Carotid arteries are his primary areas of study.
As a part of the same scientific study, Filippo Molinari usually deals with the Artificial intelligence, concentrating on Pattern recognition and frequently concerns with Speech recognition.
His Ultrasound study results in a more complete grasp of Radiology.
His Segmentation research includes elements of Ground truth, Digital pathology and Algorithm.
He has included themes like Cancer and Feature selection in his Computer vision study.
His Carotid arteries research is multidisciplinary, relying on both Ultrasonography, Asymptomatic and Ultrasound imaging.
He most often published in these fields:
- Artificial intelligence (34.24%)
- Ultrasound (23.74%)
- Segmentation (21.79%)
What were the highlights of his more recent work (between 2018-2021)?
- Artificial intelligence (34.24%)
- Segmentation (21.79%)
- Pattern recognition (15.18%)
In recent papers he was focusing on the following fields of study:
Filippo Molinari mainly investigates Artificial intelligence, Segmentation, Pattern recognition, Digital pathology and Ultrasound.
His Artificial intelligence research is multidisciplinary, incorporating perspectives in Microscope and Computer vision.
His Segmentation study incorporates themes from Similarity and Fluorescence microscope.
His Pattern recognition study integrates concerns from other disciplines, such as Breast cancer, Prostate cancer and Feature.
He interconnects Stain, Deep learning, Radiology and H&E stain in the investigation of issues within Digital pathology.
His studies in Ultrasound integrate themes in fields like Kidney disease, Sarcopenia, Cushing's disease and Biomedical engineering.
Between 2018 and 2021, his most popular works were:
- Cascaded LSTM recurrent neural network for automated sleep stage classification using single-channel EEG signals (71 citations)
- The impact of pre- and post-image processing techniques on deep learning frameworks: A comprehensive review for digital pathology image analysis. (10 citations)
- Stain Color Adaptive Normalization (SCAN) algorithm: Separation and standardization of histological stains in digital pathology. (10 citations)
In his most recent research, the most cited papers focused on:
- Internal medicine
- Artificial intelligence
- Cancer
His primary areas of investigation include Segmentation, Ultrasound, Digital pathology, Artificial intelligence and Biomedical engineering.
His work on Accurate segmentation as part of general Segmentation study is frequently connected to Context, therefore bridging the gap between diverse disciplines of science and establishing a new relationship between them.
Filippo Molinari combines subjects such as Feature and Optic nerve sheath with his study of Ultrasound.
His research investigates the link between Digital pathology and topics such as H&E stain that cross with problems in Histopathology, Algorithm, Stain and Normalization.
His Artificial intelligence research incorporates themes from Microvesicular Steatosis, Steatosis and Pattern recognition.
His Pattern recognition research is multidisciplinary, relying on both Eye movement and Non-rapid eye movement sleep.
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